It is a common paradigm in object detection frameworks to treat all samples
equally and target at maximizing the performance on average. In this work, we
revisit this paradigm through a careful study on how different samples
contribute to the overall performance measured in terms of mAP. Our study
suggests that the samples in each mini-batch are neither independent nor
equally important, and therefore a better classifier on average does not
necessarily mean higher mAP. Motivated by this study, we propose the notion of
Prime Samples, those that play a key role in driving the detection performance.
We further develop a simple yet effective sampling and learning strategy called
PrIme Sample Attention (PISA) that directs the focus of the training process
towards such samples. Our experiments demonstrate that it is often more
effective to focus on prime samples than hard samples when training a detector.
Particularly, On the MSCOCO dataset, PISA outperforms the random sampling
baseline and hard mining schemes, e.g., OHEM and Focal Loss, consistently by
around 2% on both single-stage and two-stage detectors, even with a strong
backbone ResNeXt-101.

通过在目标检测方案的样本权重分析中，提出了 Prime Sample Attention，即在训练过程中重点关注重要的样本，相较于传统随机采样和硬样本挖掘方法，可以有效提高检测器性能。